Instructions to use ISTA-DASLab/Mixtral-8x7b-AQLM-2Bit-1x16-hf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ISTA-DASLab/Mixtral-8x7b-AQLM-2Bit-1x16-hf with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ISTA-DASLab/Mixtral-8x7b-AQLM-2Bit-1x16-hf")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ISTA-DASLab/Mixtral-8x7b-AQLM-2Bit-1x16-hf")
model = AutoModelForCausalLM.from_pretrained("ISTA-DASLab/Mixtral-8x7b-AQLM-2Bit-1x16-hf", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ISTA-DASLab/Mixtral-8x7b-AQLM-2Bit-1x16-hf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ISTA-DASLab/Mixtral-8x7b-AQLM-2Bit-1x16-hf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ISTA-DASLab/Mixtral-8x7b-AQLM-2Bit-1x16-hf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ISTA-DASLab/Mixtral-8x7b-AQLM-2Bit-1x16-hf

SGLang

How to use ISTA-DASLab/Mixtral-8x7b-AQLM-2Bit-1x16-hf with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "ISTA-DASLab/Mixtral-8x7b-AQLM-2Bit-1x16-hf" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ISTA-DASLab/Mixtral-8x7b-AQLM-2Bit-1x16-hf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "ISTA-DASLab/Mixtral-8x7b-AQLM-2Bit-1x16-hf" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ISTA-DASLab/Mixtral-8x7b-AQLM-2Bit-1x16-hf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ISTA-DASLab/Mixtral-8x7b-AQLM-2Bit-1x16-hf with Docker Model Runner:
```
docker model run hf.co/ISTA-DASLab/Mixtral-8x7b-AQLM-2Bit-1x16-hf
```

Endpoint encountered an error

by vanokio - opened Feb 19, 2024

Discussion

vanokio

Feb 19, 2024

Hi!
I'm trying to deploy this model by Inference Endpoints HF and took the server with Nvidia A10G 24GB and 28GB memory, but when my model was deploying, I got an error from HF server "Endpoint failed to start". I have no clue why it happened. Please, help me with your advice :)
I have attached screenshots of the error below

BlackSamorez

IST Austria Distributed Algorithms and Systems Lab org Feb 19, 2024

Hi! You need to pass trust_remote_code=True to the model initialization somehow.
I'm not familiar with the endpoints interface so I can't tell you exactly how to do it.

BlackSamorez

IST Austria Distributed Algorithms and Systems Lab org Feb 19, 2024

I would also recommend passing low_cpu_mem_usage=True to reduce initialization time and device_map="cuda" to reduce RAM requirements significantly.

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